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Article: A new multivariate zero-adjusted Poisson model with applications to biomedicine

TitleA new multivariate zero-adjusted Poisson model with applications to biomedicine
Authors
Issue Date2019
PublisherWiley-VCH Verlag GmbH & Co. KGaA. The Journal's web site is located at https://onlinelibrary.wiley.com/journal/15214036
Citation
Biometrical Journal, 2019, v. 61 n. 6, p. 1340-1370 How to Cite?
AbstractRecently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log‐normal model (Aitchison and Ho, 1989) cannot be used to fit multivariate count data with excess zero‐vectors; (ii) The multivariate zero‐inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero‐truncated/deflated count data and it is difficult to apply to high‐dimensional cases; (iii) The Type I multivariate zero‐adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods.
Persistent Identifierhttp://hdl.handle.net/10722/258733
ISSN
2019 Impact Factor: 1.416
2015 SCImago Journal Rankings: 0.784

 

DC FieldValueLanguage
dc.contributor.authorLiu, Y-
dc.contributor.authorTian, G-
dc.contributor.authorTang, ML-
dc.contributor.authorYuen, KC-
dc.date.accessioned2018-08-22T01:43:10Z-
dc.date.available2018-08-22T01:43:10Z-
dc.date.issued2019-
dc.identifier.citationBiometrical Journal, 2019, v. 61 n. 6, p. 1340-1370-
dc.identifier.issn0323-3847-
dc.identifier.urihttp://hdl.handle.net/10722/258733-
dc.description.abstractRecently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log‐normal model (Aitchison and Ho, 1989) cannot be used to fit multivariate count data with excess zero‐vectors; (ii) The multivariate zero‐inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero‐truncated/deflated count data and it is difficult to apply to high‐dimensional cases; (iii) The Type I multivariate zero‐adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods.-
dc.languageeng-
dc.publisherWiley-VCH Verlag GmbH & Co. KGaA. The Journal's web site is located at https://onlinelibrary.wiley.com/journal/15214036-
dc.relation.ispartofBiometrical Journal-
dc.rightsThis is the peer reviewed version of the following article: Biometrical Journal, 2019, v. 61 n. 6, p. 1340-1370, which has been published in final form at https://doi.org/10.1002/bimj.201700144. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.titleA new multivariate zero-adjusted Poisson model with applications to biomedicine-
dc.typeArticle-
dc.identifier.emailYuen, KC: kcyuen@hku.hk-
dc.identifier.authorityYuen, KC=rp00836-
dc.description.naturepostprint-
dc.identifier.doi10.1002/bimj.201700144-
dc.identifier.scopuseid_2-s2.0-85047482794-
dc.identifier.hkuros286751-
dc.identifier.volume61-
dc.identifier.issue6-
dc.identifier.spage1340-
dc.identifier.epage1370-
dc.publisher.placeGermany-

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